Data Scientist Analyst

Data Science is at the core of Nielsen’s business. Our team of researchers come from diverse disciplines and they drive innovation, new product ideation, experimental design and testing, complex analysis and delivery of data insights around the world. We support all International Media clients and are located where our clients are.
What is the role?Data Science is core to what Nielsen does, and our research projects have high visibility in directly affecting the results of our business and our clients. This Data Scientist role in the Nielsen One Ads team provides an opportunity to contribute to methodological innovation and pipeline development in the exciting and fast-changing world of media measurement. This is an ideal position to grow as a researcher, contribute to innovative products, and develop subject-matter expertise in digital methodology and cross-platform audience measurement. 
Who am I working with?The Nielsen One Ads team within the Data Science Global Media organization focuses on improving and enhancing Nielsen’s highly successful products in the marketplace for digital & TV advertising. As part of this exciting team, this position will support the development and implementation of new methodologies to accurately estimate audience duplication across multiple screens (e.g. TV, PC, mobile, OTT), enhancing Nielsen's cross-media measurement capabilities. 
Why do I want to work here?As the arbiter of truth, Nielsen Global Media fuels the media industry with unbiased, reliable data about what people watch and listen to. To discover what’s true, we measure across all channels and platforms⁠—from podcasts to streaming TV to social media. And when companies and advertisers are armed with the truth, they have a deeper understanding of their audiences and can accelerate growth. 

What will I do?

• Analyze large volumes of data to identify trends and inform methodological decisions. Independently perform exploratory data analysis and present findings 
• Evaluate quality and accuracy of data sets and estimates derived from data science models
• Develop processes or code to implement, maintain, and enhance methodologies in client-facing products
• Act as a liaison and subject matter expert with key internal stakeholders (e.g. application development, engineering, client business partners)
• Stay up to date on industry changes to digital measurement (e.g. new devices and platforms, privacy law updates, changes in browser/app measurement, etc.) and critically assess how it would impact Nielsen measurement
• Stay informed of new research and developments in the field, as well as participate in internal and external knowledge exchanges (conferences, workshops, webinars)  

Is this for me?

• This position requires a detail-oriented person who has experience in big data analysis using multiple data sources and statistical research, and who enjoys working in a fast-paced environment.
• The ideal candidate will have experience with short-term analyses / prototyping, as well as writing scalable code that can be deployed in production as part of broader data pipelines  
• Graduate degree in statistics, mathematics, economics, operations research, quantitative social sciences (e.g. psychology, sociology, etc.) or hard sciences (e.g. engineering, computer science, etc.) OR undergraduate degree and 2+ years of relevant experience with strong analytical expertise 
• Proficiency in Python and SQL
• Experience with core Data Science tech stack (pandas, pyspark, scikit learn)
• Strong communication & presentation skills (written and verbal) 

Preferred Skills:

• Industry knowledge of digital audience / media measurement
• Experience working independently and as part of cross-functional teams
• Experience with statistical estimation, classification, regression, and machine learning techniques, including Generalized Linear Models, Decision Trees, Neural Networks, Variational Inference, as well as sampling and weighting techniques
• Experience wrangling, analyzing, and correcting very large datasets using statistical models 
• Familiarity with working with big data in cloud environments (e.g. AWS, Databricks)
• Familiarity with source control using git and CI/CD workflows
• Familiarity with data visualization tools (e.g. Matplotlib, ggplot, plotly, Tableau)